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A mean value turbocharged engine model is useful in terms of accuracy and convenience for fuel economy strategies or engine controller development. Turbocharging process is a feedback system with a positive gain, i.e. increasing exhaust work leads to increasing a cycle work. The gain of the feedback system is determined mainly by exhaust work ratio in a cycle and inertia of the turbine. The work ratio was investigated based on engine test with EGR. A turbocharging process model was obtained using the work ratio in a cycle and theoretical equations. The model is applied to investigate manifold absolute pressure response with EGR.

Downsizing engines with a turbocharging system have been widely applied to passenger cars to improve fuel economy. Engine torque response to accelerator operation is one of important features in addition to steady state performance of the system. Torque profile management for turbocharged internal combustion engines is one of required technologies. A turbocharging system for a car is a system with a positive feedback loop in which compressed air drives the compressor after the combustion process. A reduced order model was derived for the charging system. Pressure ratio of a compressor is proportional to square of turbine speed and the turbine speed is a first order delay system to throttle opening in the model. Model structure was designed from mathematical equations that describe turbine and compressor works. Model parameters were identified from measured data. An output torque profile control strategy based on the derived model is investigated.

Driving car means to control a vehicle according to a target path, e.g. road and speed, with some constraints. Human driving models have been proposed and applied for simulations. However, human control in driving has not been analyzed sufficiently comparing with that of machine control system in term of control theory. Input - output property with internal information processing is not easily measured and described. Response of human driving is not as quicker as that of machine controller but human can learn vehicle response to driving operation and predict target changes. Driving behavior of an expert driver and a beginner in an emission test cycle was measured and difference in target speed tracking was looked into with performance indices. The beginner's operation was less stable than that of the expert. Transfer function of the vehicle system was derived based on linearized model to investigate human driving behavior as a tracking controller in the system.